RNAseq
Quality check from Kallisto output
This information is coupled with the multiQC report generated by the pipeline. The percentage of pseudoaligned reads is more homogeneous and higher when the paired-end protocol is used. Looking at all the figures generated, the results seem to be of better quality when using the paired-end protocol.
Protocol
n_targets
n_processed
n_pseudoaligned
n_unique
p_pseudoaligned
p_unique
Protocol + Condition
n_targets
n_processed
n_pseudoaligned
n_unique
p_pseudoaligned
p_unique
Differental Expression : Treated vs Untreated
Single-end
Results :
- DEGs number : 1931 (padj < 0.05)
- Up-regulated in Treated condition : 1055
- Down-regulated in Treated condition : 876
- Positive logFC = up-regulated in Treated condition / down-regulated in Untreated condition
- Negative logFC = up-regulated in Untreated condition / down-regulated in Treated condition
Dataset
Liste
Outlier
Heatmap
PCA
Expression
Volcano
Top20
TopHeatmap
MDplot
Paired-end
Results :
- DEGs number : 3346 (padj < 0.05)
- Up-regulated in Treated condition : 1773
- Down-regulated in Treated condition : 1573
- Positive logFC = up-regulated in Treated condition / down-regulated in Untreated condition
- Negative logFC = up-regulated in Untreated condition / down-regulated in Treated condition
Dataset
Liste
Outlier
Heatmap
PCA
Expression
Volcano
Top20
TopHeatmap
MDplot
Comparaison Single-end vs Paired-end
Intersection of the DEA result
There are more differentially expressed transcripts detected between the Treated / Untreated conditions using the Paired-end protocol. This seems logical as the data from the paired-end protocol are more homogeneous as shown on the kalisto output concerning the percentage of aligned reads.
What’s more, when the treated protocol is used for PCA, 97% of the treated/non-treated variability is explained, compared with 89% for the single-end protocol. The biological variability that interests us most is much more concentrated than when the single-end protocol is used. As the treatment effect is much more highlighted in the Paried-end protocol, the DEA results will be much more interesting to exploit. The clarity and uniformity of the variability explained by the treatment condition in the paired-end condition may also explain why we obtain a greater number of differentially expressed transcripts compared with the single-end protocol.
The single-end protocol shows that there is more noise in the overall results and is therefore less interesting to use for this type of analysis.
In conclusion, there is a very clear effect of treatment on the individuals sequenced here, whatever the protocol used. It’s just that this effect looks much better highlighted in the data when the paired-end protocol is used.
Multivariate model (EXPERIMENTAL)
Dataset
Outlier
Heatmap
PCA
MDplot
Treated vs Untreated
Results :
- DEGs number : 4106 (padj < 0.05)
- Up-regulated in Treated condition : 2034
- Down-regulated in Treated condition : 2072
- Positive logFC = up-regulated in Treated condition / down-regulated in Untreated condition
- Negative logFC = up-regulated in Untreated condition / down-regulated in Treated condition
Expression
Volcano
Top20
TopHeatmap
Single-end vs Paired-end
Results :
- DEGs number : 5931 (padj < 0.05)
- Up-regulated in Treated condition : 2457
- Down-regulated in Treated condition : 3474
- Positive logFC = up-regulated in Treated condition / down-regulated in Untreated condition
- Negative logFC = up-regulated in Untreated condition / down-regulated in Treated condition